Engineering Glossary

A variation of Git pull that fetches remote changes and rebases local commits on top of them instead of merging.

A safer alternative to force push, which only updates the branch if it hasn't been modified since your last fetch.

A command that compares the changes between two commit ranges, useful for reviewing rebased branches.

A low-level command that reads tree objects into the index, used in complex merging scenarios.

A log of all reference updates in a repository, useful for recovering lost commits or branches.

Same as Git Reflog, logs that record when the tips of branches and other references were updated in the repository.

The system Git uses to store references (like branches and tags) as files in the .git directory.

A string that specifies the mapping between remote and local references when fetching or pushing.

Adds a new remote repository to your local Git configuration, associating a name with a URL.

A command that removes remote-tracking branches that no longer exist on the remote repository.

Removes a remote repository from your local Git configuration, deleting its associated references.

A feature allowing one Git object to be transparently used in place of another, useful for history rewriting.

A feature that remembers how you resolved merge conflicts and automatically reapplies the resolution.

A command used to undo changes by moving the current branch head to a specific commit.

A Git reset option that updates the working directory and staging area to match the specified commit.

A Git reset option that moves the HEAD to a specified commit without changing the working directory or staging area.

A command that lists commit objects in reverse chronological order, often used as a basis for other commands.

A command used to parse Git revision syntax and return the corresponding object name.

A command that creates a new commit that undoes the changes made by a previous commit.

A command to remove files from both the working directory and the Git repository.

A clone that only fetches a limited number of commits, reducing download size and time for large repositories.

A command that summarizes git log output, typically used to create release announcements.

Commits that are cryptographically signed to verify the identity of the committer and ensure integrity.

Tags that are cryptographically signed to verify the identity of the tagger and ensure the tag's integrity.

The process of combining multiple commits into a single commit, often used to simplify history before merging.

A command that applies a stored stash to the working directory without removing it from the stash list.

A command that applies a stored stash to the working directory and removes it from the stash list.

A command that displays the state of the working directory and the staging area.

A repository embedded within another repository, allowing you to keep a Git repository as a subdirectory of another Git repository.

A command that executes a specified shell command for each initialized submodule in a Git repository.

A feature for managing nested repositories as subdirectories of a parent repository, an alternative to submodules.

A low-level Git command that manipulates the staging area, updating file information and preparing changes for commit.

The communication protocol used between Git clients and servers for efficient data transfer.

A feature allowing multiple working trees to be attached to the same repository, useful for working on different branches simultaneously.

Creates a new working tree associated with the repository, allowing work on multiple branches simultaneously.

A command to list details of each working tree associated with the repository, showing paths and branch information.

A low-level Git command that creates a tree object from the current index, representing a directory structure.

A Git command used to create an archive (zip or tar) of files from a specified commit, useful for creating release packages.

Agreed-upon patterns for naming branches to improve organization and clarity in collaborative projects.

A command to examine the contents and type of Git objects, useful for inspecting repository internals.

A command to resume the cherry-pick process after resolving conflicts in a paused cherry-pick operation.

Levels at which Git configuration can be set: system (all users), global (user), and local (repository).

A command providing detailed statistics about the number and size of objects in a Git repository.

Mechanisms to securely store and retrieve credentials for Git operations, reducing manual authentication.

Custom scripts that determine how Git should generate diffs for specific file types.

A versatile tool for rewriting Git repository history, offering more power and flexibility than git filter-branch.

A more thorough garbage collection process in Git, optimizing repository storage and performance.

A low-level Git command that computes the object ID (SHA-1 hash) for a given file or string, used in Git's internal storage.

Scripts triggered by specific events in Git workflows, used to automate tasks or enforce policies.

The intermediate area in Git where changes are prepared before committing, also known as the staging area.

Individual, uncompressed Git objects stored separately in the object database before being packed.

A command to list the contents of a tree object in Git, showing files and subdirectories.

Custom scripts defining how Git should handle merges for specific file types or patterns.

A low-level Git command that creates a tree object from a specific index or tree listing.

The four fundamental object types used by Git to store repository data and history.

A command to pack references into a single file for improved performance in repositories with many references.

Compressed files containing multiple Git objects, optimizing storage and transfer efficiency in repositories.

Server-side scripts that run after commits have been accepted, often used for notifications or deployments.

Server-side scripts that run before pushed commits are accepted, used to enforce policies or checks.

An improved version of the Git transfer protocol, offering better performance and feature negotiation.

A command to move a series of commits to a new base commit, useful for changing the parent of a branch.

A command to remove old entries from the reflog, useful for cleaning up Git history and freeing space.

A command to combine all loose objects in a repository into packfiles, improving storage efficiency.

Commands to undo changes in Git, with varying impacts on the working directory and staging area.

Techniques and tools for securely storing and accessing sensitive information in Git repositories.

A command that lists references in a Git repository along with their associated commit hashes.

A feature allowing users to check out only a subset of files from a repository, saving space and time.

A command to validate the contents of Git pack files, ensuring data integrity in the object database.

An application that can be installed directly on organizations and user accounts to extend GitHub's functionality.

A version of Markdown with additional features specifically supported on GitHub.

A tool for importing repositories to GitHub from other version control systems or source code hosts.

A feature in GitHub for tracking tasks, enhancements, and bugs for a project.

A platform for posting and finding software development jobs, integrated with GitHub profiles, that was discontinued in 2021.

A platform for developers to sell and buy integrations for GitHub, enhancing workflow and productivity.

A static site hosting service that takes files directly from a GitHub repository to publish websites.

A collaborative documentation feature in GitHub repositories, allowing easy creation and editing of project documentation.

A lightweight, branch-based workflow that supports teams and projects where deployments are made regularly.

A version control workflow that combines feature-driven development and feature branches with issue tracking.

Branching model for Git that defines a strict branching model designed around project releases.

A graphical interface for viewing Git repositories, providing a visual representation of commit history.

Query language for APIs and a runtime for executing those queries with existing data.

A pointer in Git that refers to the latest commit in the current checked-out branch.

A simple program often used as an introduction to a new programming language or environment, including Git tutorials.

A method for quickly patching production releases by branching directly from the main branch or a tag.

Also known as the staging area, where changes are prepared before committing in Git.

Predefined structures for creating new issues in Git repositories, ensuring consistent and complete information.

A static site generator often used with GitHub Pages to create websites from Markdown files in a Git repository.

A tool in GitHub Pages that allows users to easily select and apply pre-designed themes to their Jekyll sites.

Git Large File Storage, an extension for versioning large files in Git repositories.

A library used by GitHub to detect the programming languages used in repositories.

A lightweight markup language commonly used for documentation in Git repositories.

Formerly the default name for the primary branch in Git repositories, now often replaced by 'main'.

The process of combining changes from different branches into a single branch, integrating multiple lines of development.

An application that uses the OAuth protocol to access Git platform APIs on behalf of users.

A secure string used for authentication in Git operations, allowing access to resources without sharing passwords.

The default name for the remote repository from which a local repository was cloned.

A file describing changes between versions, used to share and apply modifications across different Git repositories.

A Git command that fetches changes from a remote repository and merges them into the current branch.

A method of submitting contributions to a Git project, proposing changes from a forked repository or branch.

Customizable files that automatically populate the body of new pull requests, guiding contributors.

High-density memory architecture using vertically stacked chips, enhancing performance and capacity in cloud server hardware.

Cloud infrastructure optimized for 5G networks, supporting high-speed, low-latency services and applications.

Technique to create multiple virtual networks on a shared physical 5G infrastructure, each optimized for specific use cases.

Integration of 5G networks with edge computing to enable low-latency, high-bandwidth applications closer to end-users.

Future convergence of 6G networks with cloud computing, promising ultra-high speeds and advanced capabilities.

Database transaction properties (Atomicity, Consistency, Isolation, Durability) ensuring data integrity in cloud environments.

Software ensuring AI systems adhere to ethical guidelines and regulations in cloud deployments.

Software for identifying and mitigating ethical issues and biases in AI models deployed in cloud environments.

Solutions for managing ethical considerations and regulatory compliance in cloud-based AI systems.

Structured approaches for managing AI development, deployment, and use in cloud environments.

Comprehensive solutions for managing, monitoring, and controlling AI models in cloud-based systems.

Cloud-based tools for explaining and understanding the decision-making processes of AI models.

Software for analyzing and explaining AI model decisions, crucial for transparency in cloud AI services.

Cloud platforms for discovering, sharing, and monetizing pre-trained AI models and algorithms.

Tools for tracking AI model performance and identifying deviations from expected behavior in cloud environments.

Systems for managing different versions of AI models and enforcing governance policies in cloud deployments.

Cloud-based development environments that use AI to assist programmers in writing and optimizing code.

Integration of AI capabilities into data analytics processes in cloud environments for enhanced insights.

Use of AI algorithms to predict and optimize resource allocation in cloud infrastructures.

Application of AI techniques to improve efficiency, performance, and cost-effectiveness of cloud resources.

Automated distribution of cloud resources using AI to optimize performance and cost-efficiency.

AI-based automation of cloud service selection and integration for complex workflows.

Automated creation of source code using AI models, often integrated into cloud development platforms.

Automated categorization and labeling of data using AI algorithms in cloud storage and processing systems.

Use of AI to improve network performance, efficiency, and security in cloud environments.

Intelligent distribution of computing resources in cloud environments using AI algorithms.

Enhanced SIEM systems using AI for improved threat detection and response in cloud environments.

Use of AI algorithms to identify and analyze potential security threats in cloud systems.

Proactive search for hidden threats in cloud environments using AI-powered analytics and automation.

Cloud infrastructure components designed or configured to enhance AI workload performance.

Database systems tailored for AI workloads, often featuring in-memory processing and distributed architectures.

Use of AI algorithms to identify unusual patterns or behaviors in cloud systems and data.

Intelligent automation of data and application integration processes in cloud environments using AI.

Cloud-based offering of AI capabilities, allowing businesses to leverage AI without extensive in-house expertise.

Cloud-based AI capabilities offered as a service, enabling easy integration of AI into applications.

Application of AI for automating and enhancing IT operations management in cloud environments.

Integrated solutions leveraging AI for automated monitoring, analysis, and management of cloud IT operations.

Use of AI-driven analytics to forecast and prevent system failures in cloud infrastructure.

Service that protects APIs in cloud environments by managing access, monitoring traffic, and preventing attacks.

Dedicated services for securing and managing APIs in cloud environments, including access control and threat protection.

Design approach prioritizing API creation before implementation, common in cloud-native application development.

Integration strategy using purpose-built APIs to connect data, devices, and applications in cloud ecosystems.

Persistent 3D digital content overlaid on the physical world, accessible via cloud infrastructure for augmented reality applications.

Cloud-based tools for analyzing user behavior and performance in augmented and virtual reality environments.

Cloud-hosted services enabling multi-user interaction in shared augmented or virtual reality spaces.

Specialized CDN optimized for delivering AR/VR content with low latency and high bandwidth.

Cloud-based tools and services for creating, testing, and deploying augmented and virtual reality applications.

JSON-based files defining infrastructure and configuration for Azure resource deployment.

Dynamic UIs that adjust based on user behavior, device capabilities, and context in cloud applications.

Techniques to identify and mitigate attacks on ML models in cloud-based AI systems.

Systems for informing administrators or users about important events or issues in cloud environments.

Process of examining AI algorithms for bias, errors, or unintended consequences in cloud-based systems.

Seamless, context-aware interaction methods for accessing cloud services in IoT environments.

Tools for identifying unusual patterns or behaviors in cloud systems, often using machine learning techniques.

Network management approach that optimizes performance based on specific application requirements in cloud environments.

Technique for quickly estimating query results in large-scale cloud databases, trading accuracy for speed.

Low-cost cloud storage for infrequently accessed data with longer retrieval times, e.g., Amazon Glacier, Azure Archive Storage.

Cloud-based storage for software build outputs, dependencies, and related metadata.

Hypothetical cloud offering of human-level AI capabilities across various domains.

Cloud-based environments for developing and testing advanced AI systems approaching human-level intelligence.

Systematic recording of actions and events in cloud systems for security and compliance purposes.

Chronological records of system activities for reconstructing and examining the sequence of events in cloud environments.

Integration of machine learning and natural language processing in cloud-based data analytics workflows.

Shared, persistent digital content overlaid on the physical world, accessible via cloud infrastructure.

Cloud-based systems for managing and delivering AR content and experiences, enabling scalable AR applications.

Cloud-powered generation of AR graphics and content, offloading processing from end-user devices.

Collections of EC2 instances that automatically adjust capacity based on defined conditions.

Automatic adjustment of cloud resources to match workload demands, ensuring optimal performance and cost-efficiency.

Automated machine learning processes for model selection, hyperparameter tuning, and feature engineering in cloud environments.

Cloud-based services automating the machine learning pipeline from data preparation to model deployment and monitoring.

AI-driven tools for improving efficiency and performance of machine learning workflows in the cloud.

Systems that automatically implement and maintain cloud resource policies and compliance.

Continuous, AI-driven assessment of cloud systems against regulatory and security standards.

AI-powered tools for identifying, categorizing, and mapping data assets in cloud environments.

AI-driven systems for managing data quality, security, and compliance in cloud environments.

Cloud-based tools using AI to clean, transform, and prepare data for analysis, streamlining data preparation processes.

AI-powered generation and selection of features for machine learning models in cloud environments.

AI-driven coordination of security incident detection and resolution in cloud systems, automating response workflows.

Cloud services that automate the process of creating and optimizing machine learning models, from data prep to deployment.

Cloud-based systems that automate the end-to-end machine learning model development process, including feature engineering and model selection.

AI-driven tools for simulating cyberattacks to identify vulnerabilities in cloud systems and applications.

AI-powered analysis of cloud architectures to identify potential security risks and attack vectors.

Self-managing, self-healing cloud services requiring minimal human intervention, leveraging AI for operations.

Ultra-low latency edge computing extensions of Azure for 5G networks, bringing cloud resources closer to users.

Service for creating, assigning, and managing policies to control Azure resources and ensure compliance.

Cloud-based services facilitating data exchange and process integration between business partners.

Architectural approach creating backend services tailored to specific frontend application needs in cloud environments.

Physical server dedicated to a single tenant, offering direct hardware access in cloud environments.

Dedicated physical servers without virtualization, offered as a cloud service for high-performance workloads.

Execution of series of jobs without user interaction, often for large-scale data processing in the cloud (e.g., AWS Batch, Azure Batch).

Distributed computing environments for processing and analyzing massive datasets in the cloud.

Interface for monitoring and managing cloud service usage and costs, providing detailed breakdowns and forecasts.

Use of cloud resources for computational biology and genomics research, enabling large-scale analysis and modeling.

Evaluation of cloud data center effects on local ecosystems and wildlife to ensure sustainable operations.

Cloud storage that provides fixed-size raw storage volumes, typically used for databases or file systems.

Cloud services providing persistent block-level storage volumes for use with compute instances.

Cloud-based tools for analyzing and visualizing blockchain data and transactions, offering insights into network activity.

Distributed databases using blockchain technology for enhanced security and immutability in cloud environments.

Software for managing and enforcing rules, permissions, and consensus mechanisms in blockchain networks deployed on cloud platforms.

Cloud-based solutions for connecting blockchain networks with existing enterprise systems and applications.

Design pattern using DaemonSets to run system daemons or agents on every node in a cluster.

Processes and tools for preserving and restoring data in containerized environments, ensuring data durability.

Techniques ensuring data integrity across distributed container-based applications, maintaining reliability.

Distributed data management architecture providing consistent data access across containerized applications.

Unified data architecture combining data lake and data warehouse features in containerized environments.

Process of moving data between containers or from traditional systems to containerized applications.

Techniques for maintaining data across container lifecycles, including volume mounts and persistent volumes.

Network layer responsible for forwarding container traffic based on control plane decisions.

Techniques for creating and maintaining copies of data across multiple containerized instances.

Partitioning data across multiple database containers for improved scalability and performance.

Process of running database systems within containers for improved portability and resource utilization.

Microservices design pattern where each service has its own dedicated database, ensuring data isolation.

Platform for observability and monitoring of containerized environments and microservices.

Design pattern for handling failed message processing in containerized message-driven systems.

Approach to deploying containers by specifying desired state rather than imperative commands.

Techniques for rolling out container updates, including rolling updates, blue-green, and canary deployments.

Kubernetes component that evicts pods from nodes based on specific policies to optimize cluster resource usage.

Standardized format defining the structure and metadata of Docker container images, ensuring portability.

Kubernetes feature allowing nodes to advertise system hardware resources to the cluster, enabling specialized hardware use.

Network optimization technique in container load balancing for improved performance by bypassing the load balancer for responses.

Implementation of tracing in containerized microservices for end-to-end request visibility.

Implementation of distributed tracing using Jaeger to monitor and optimize performance in microservices architectures.

Implementation of OpenTelemetry for standardized observability in containerized environments.

Minimal container images containing only the application and its runtime dependencies, reducing attack surface.

Command to attach local standard input, output, and error streams to a running container for interaction and debugging.

Set of files located in the specified PATH or URL, used during the docker build process to create images.

Docker CLI plugin for extended build capabilities, including multi-platform builds and enhanced build options.

Component enabling Docker to be used as a container runtime in Kubernetes via the Container Runtime Interface.

Command-line tool for interacting with Docker daemon and managing containers, images, and other Docker objects.

Command creating a new image from a container's changes, useful for saving modifications made to a running container.

Tool for defining and running multi-container Docker applications, simplifying the process of managing complex application stacks.

Process of viewing detailed information about a container's configuration and state, aiding in troubleshooting.

Feature providing digital signing and verification of Docker images, ensuring image authenticity and integrity.

Process of validating the authenticity and integrity of signed Docker images before use.

Feature allowing management of multiple Docker endpoints from a single Docker client, simplifying multi-environment management.

Background service managing Docker objects like images, containers, networks, and volumes.

Command allowing users to run a new process inside a running container, useful for debugging and maintenance tasks.

Commands for exporting a container's filesystem as a tarball and importing it as an image.

Cloud-based repository service for finding and sharing container images, central to the Docker ecosystem.

Command showing the history of an image's layers, providing insight into how the image was built and modified.

Process of examining Docker image metadata, including layers, environment variables, and exposed ports.

Standardized format defining the structure and metadata of Docker container images, ensuring portability.

Virtualized network providing connectivity between Docker containers, isolating container traffic from the host network.

Different networking modes in Docker, including bridge, host, overlay, and macvlan, offering varied connectivity options.

Command for removing unused Docker objects like containers, networks, images, and volumes.

Commands for exporting Docker images to tar archives and importing them back, useful for image transfer and backup.

Command-line tool for scanning Docker images for vulnerabilities, providing detailed reports on potential security issues.

Deprecated component that provided CRI compatibility for Docker in Kubernetes, bridging Docker and CRI.

Unix socket or named pipe used for communication between Docker client and daemon, enabling Docker API access.

Command displaying a live stream of container resource usage statistics, including CPU, memory, and network I/O.

Native clustering and orchestration solution for Docker, allowing you to create and manage a swarm of Docker nodes as a single virtual system.

Real-time stream of Docker daemon events, providing insights into container, image, volume, and network activities.

Command displaying the running processes of a container, similar to the Unix top command but container-specific.

Persistent data storage mechanism for containers, independent of container lifecycle, enabling data persistence.

Different storage backends for Docker volumes, including local, NFS, and cloud-specific drivers.

Text document containing all the commands a user could call on the command line to assemble a Docker image.

Instruction defining build-time variables that users can pass at build-time to the builder.

Guidelines for creating efficient, secure, and maintainable Dockerfiles, optimizing image builds and runtime performance.

Instruction telling Docker how to test a container to check its health status, improving reliability.

Set of commands used in Dockerfiles to build images, including ADD, COPY, RUN, CMD, and ENTRYPOINT.

Set of commands used in Dockerfiles to build images, including ADD, COPY, RUN, CMD, and ENTRYPOINT.

Instruction setting the system call signal that will be sent to the container to exit, customizing shutdown behavior.

File specifying which files and directories should be excluded when building a Docker image.

Approach to software design focusing on the core domain in containerized microservices architectures.

P2P-based image and file distribution system to improve efficiency of image downloads in container environments.

Process of cordoning a node and evicting its pods in preparation for maintenance or decommissioning.

Networking configuration supporting both IPv4 and IPv6 in container environments, enabling broader connectivity options.

Kubernetes feature allowing custom logic to be applied to object creation or modification requests.

Kubernetes feature enabling runtime configuration of audit policies without API server restart.

Automatic creation of storage when persistent volume claims are made in Kubernetes, simplifying storage management.

Monitoring solution for containerized environments, providing deep visibility into application performance.

Combination of Elasticsearch, Logstash, and Kibana for logging and monitoring containerized environments.

Network communication between containers or services within the same cluster or data center.

Deployment of artificial intelligence and machine learning models on edge devices using containers.

Processing and analyzing data at the network edge using containerized analytics applications.

Tools and practices for managing containerized workloads on edge devices, ensuring consistent deployment and updates.

Management and coordination of containerized applications across distributed edge locations.

Security measures and practices specific to containerized edge computing environments, protecting distributed workloads.

Techniques for efficiently distributing containerized workloads across edge devices, optimizing resource usage.

Synchronization of data and state between edge containers and cloud-based systems, ensuring consistency.

Component controlling outbound traffic from a container cluster to external services, enhancing security and traffic management.

Service mesh components managing outbound traffic from the mesh to external services, providing fine-grained control.

Management and policies for outbound network traffic from containers, ensuring security and compliance.

Application Performance Monitoring tool for containerized applications in the Elastic Stack.

Sensitive data stored in an encrypted form in container orchestration platforms, enhancing security.

Comprehensive testing of containerized applications from start to finish, validating entire system functionality.

Kubernetes API resource providing a more scalable alternative to Endpoints for service discovery.

Key-value pairs passed to containers at runtime, used for configuration and runtime behavior control.

High-performance proxy often used in service mesh implementations for containerized environments.

Temporary containers used for troubleshooting or debugging in Kubernetes pods, without affecting the main containers.

Short-lived storage volumes in Kubernetes that share the lifecycle of a pod, useful for temporary data storage.

Routing strategy for load balancing network traffic across multiple paths in container networks.

Distributed key-value store that provides a reliable way to store data across a cluster of machines.

Destinations for cluster-level events in container orchestration platforms, used for monitoring and alerting.

Components or services generating events in containerized environments, often used in event-driven architectures.

Pattern of storing changes to application state as a sequence of events, often used in microservices architectures.

Design pattern capturing all changes to application state as a sequence of events, useful in distributed systems.

Implementation of event sourcing pattern in containerized microservices architectures, ensuring data consistency.

Design approach where containerized services produce, detect, and react to events, enabling loose coupling and scalability.

Rules determining which pods to terminate when a node is under resource pressure in Kubernetes clusters.